An InterpreTable Fuzzy-Logic Framework for Diagnosis and Fault-Signature Analysis of Operational, Corrosion, Structural, and Metallurgical Failure Scenarios in an Aged Pipeline Network

Aged pipeline networks face interacting operational, electrochemical, structural, and metallurgical degradation mechanisms that are often assessed independently, limiting diagnosis of system-wide failure propagation. This study develops an integrated, interpreTable fuzzy-logic framework to detect, quantify, and isolate failure scenarios in a 572 km pipeline network. The methodology combines 596 historical SCADA and SAP records, cathodic-protection and soil measurements from 907 evaluation points, 4651 ultrasonic-inspection anomalies, mechanical and metallographic characterization of API L X65 steel, and validated expert knowledge. Four Mamdani fuzzy inference systems were constructed to represent operational capacity, cathodic-protection performance, mechanical integrity, and metallurgical degradation. Their outputs were combined into a unified fault-signature matrix comprising 28 failure scenarios. Scenario FS-3 produced the broadest systemic response by activating all diagnostic residuals. Metallurgical scenarios FS-22 to FS-28 activated 61% of the matrix, showing their extensive influence on pipeline integrity. Structural scenarios FS-15 to FS-21 showed progressively broader signatures as wall deterioration increased. Meanwhile, FS-1, FS-2, FS-5, FS-6, and FS-7 remained localized and were more readily isolated. The proposed framework preserves diagnostic traceability through explicit input variables, fuzzy rules, and residual signatures. It provides an interpreTable basis for failure classification, diagnostic signature breadth, maintenance prioritization, and operator decision support. Its conclusions are limited to the analyzed network and require external validation before application to other pipeline systems.